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Identifying a novel 5-gene signature predicting clinical outcomes in acute myeloid leukemia

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Abstract

Background

Acute myeloid leukemia (AML) is the most common type of acute leukemia and biologically heterogeneous diseases with poor prognosis. Thus, we aimed to identify prognostic markers to effectively predict the prognosis of AML patients and eventually guide treatment.

Methods

Prognosis-associated genes were determined by Kaplan–Meier and multivariate analyses using the expression and clinical data of 173 AML patients from The Cancer Genome Atlas database and validated in an independent Oregon Health and Science University dataset. A prognostic risk score was computed based on a linear combination of 5-gene expression levels using the regression coefficients derived from the multivariate logistic regression model. The classification of AML was established by unsupervised hierarchical clustering of CALCRL, DOCK1, PLA2G4A, FCHO2 and LRCH4 expression levels.

Results

High FCHO2 and LRCH4 expression was related to decreased mortality. While high CALCRL, DOCK1, PLA2G4A expression was associated with increased mortality. The risk score was predictive of increased mortality rate in AML patients. Hierarchical clustering analysis of the five genes discovered three clusters of AML patients. The cluster1 AML patients were associated with lower cytogenetics risk than cluster2 or 3 patients, and better prognosis than cluster3 patients (P values < 0.05 for all cases, fisher exact test or log-rank test).

Conclusion

The gene panel comprising CALCRL, DOCK1, PLA2G4A, FCHO2 and LRCH4 as well as the risk score may offer novel prognostic biomarkers and classification of AML patients to significantly improve outcome prediction.

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Availability of data and material

The datasets generated and/or analysed during the current study are available upon reasonable request.

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Authors and Affiliations

  1. Department of Hematology, The Affiliated People’s Hospital of Ningbo University, No. 251, East Baizhang Road, Ningbo, 315000, Zhejiang, China

    K. Sha, Y. Lu, P. Zhang, R. Pei, X. Shi, Z. Fan & L. Chen

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  1. K. Sha
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  2. Y. Lu
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  3. P. Zhang
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  7. L. Chen
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Contributions

KS designed the study. RP, KS, LC downloaded somatic mutation, RNA-seq data and clinical data from the TCGA and OHSU databases. YL, PZ, RP, XS performed the survival analyses and differentially expressed gene analysis. ZF, LC conducted unsupervised hierarchical clustering analysis. KS prepared the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to K. Sha.

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Conflict of interest

The authors declare no competing interests.

Ethical approval

The study was approved by the Ethics Committee of the Department and conformed to the ethical guidelines of the Helsinki Declaration (as revised in Tykyo 2004).

Informed consent

All participants have provided written informed consent in this study.

Code availability

The codes in the current study are available upon reasonable request.

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Electronic supplementary material

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Supplementary file1 (DOCX 27 kb)

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Supplementary file2 Supplementary Figure 1. The overlap of prognosis-associated genes between TCGA and OHSU datasets. A. The overlap of protective type genes between TCGA and OHSU datasets, A. The overlap of risk type genes between TCGA and OHSU datasets. (TIF 1959 kb)

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Supplementary file3 Supplementary Figure 2. Kaplan-Meier survival analysis of patients’ OS with CALCRL (A), DOCK1 (B), FCHO2 (C) and LRCH4 (D) and PLA2G4A (E) expression levels in 405 AML patients of the OHSU dataset. (TIF 1232 kb)

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Supplementary file4 Supplementary Figure 3. The mutation frequencies of DOCK1 (A), FCHO2 (B) and PLA2G4A (C) in the TCGA and OHSU datasets. (TIF 1898 kb)

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Supplementary file5 Supplementary Figure 4. Unsupervised hierarchical clustering of the 5-gene panel uncovered three classes of AML patients in the TCGA dataset. (JPEG 1663 kb)

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Supplementary file6 Supplementary Figure 5. Unsupervised hierarchical clustering of the 5-gene panel uncovered three classes of AML patients in the OHSU dataset. (JPEG 978 kb)

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Supplementary file7 Supplementary Figure 6. The three clusters of AML patients exhibited significant differences in FLT3-ITD mutation in the OHSU dataset. (TIF 1847 kb)

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Sha, K., Lu, Y., Zhang, P. et al. Identifying a novel 5-gene signature predicting clinical outcomes in acute myeloid leukemia. Clin Transl Oncol 23, 648–656 (2021). https://doi.org/10.1007/s12094-020-02460-1

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  • DOI: https://doi.org/10.1007/s12094-020-02460-1

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